Neural signals are extremely variable. Understanding the variability and harnessing its rich content lie at the very heart of contemporary neuroscience. In this application we propose to tackle 1 aspect of this vast field: estimation of event related field potential signals on a trial-by-trial basis and applying the estimated single trial event related parameters to address questions related to functions of neural systems. There are 2 Specific Aims.
In Aim 1 we propose to further develop and thoroughly validate a single-trial analysis methodology termed differentially variable component analysis (dVCA).
In Aim 2 we propose to apply the methodology to analyze local field potentials from 2 existing datasets: 1 from macaque monkeys performing a visuomotor pattern discrimination task and the other from macaque monkeys performing an intermodal (visual versus auditory) selective attention task. The first dataset is unique in that it consists of local field potentials simultaneously recorded from up to 16 bipolar intracortical electrodes chronically implanted in one hemisphere, and is therefore ideally suited for addressing issues related to timing and large-scale networking of neural activations and their task relevance. The second dataset is unique in consisting of local field potentials recorded simultaneously from multiple contacts along a linear electrode spanning all the cortical layers in the primary visual cortex. Our goal is to study how visual information processing is modulated by attention.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH070498-02
Application #
7012761
Study Section
Special Emphasis Panel (ZRG1-IFCN-E (02))
Program Officer
Quinn, Kevin J
Project Start
2005-02-01
Project End
2008-01-31
Budget Start
2006-02-01
Budget End
2007-01-31
Support Year
2
Fiscal Year
2006
Total Cost
$191,810
Indirect Cost
Name
University of Florida
Department
Biomedical Engineering
Type
Schools of Engineering
DUNS #
969663814
City
Gainesville
State
FL
Country
United States
Zip Code
32611
Wen, Xiaotong; Rangarajan, Govindan; Ding, Mingzhou (2013) Multivariate Granger causality: an estimation framework based on factorization of the spectral density matrix. Philos Trans A Math Phys Eng Sci 371:20110610
Mo, Jue; Schroeder, Charles E; Ding, Mingzhou (2011) Attentional modulation of alpha oscillations in macaque inferotemporal cortex. J Neurosci 31:878-82
Nedungadi, Aatira G; Rangarajan, Govindan; Jain, Neeraj et al. (2009) Analyzing multiple spike trains with nonparametric Granger causality. J Comput Neurosci 27:55-64
Xu, Luzhou; Stoica, Petre; Li, Jian et al. (2009) ASEO: a method for the simultaneous estimation of single-trial event-related potentials and ongoing brain activities. IEEE Trans Biomed Eng 56:111-21
Fogelson, Noa; Wang, Xue; Lewis, Jeffrey B et al. (2009) Multimodal effects of local context on target detection: evidence from P3b. J Cogn Neurosci 21:1680-92
Nalatore, Hariharan; Ding, Mingzhou; Rangarajan, Govindan (2009) Denoising neural data with state-space smoothing: method and application. J Neurosci Methods 179:131-41
Zhang, Y; Chen, Y; Bressler, S L et al. (2008) Response preparation and inhibition: the role of the cortical sensorimotor beta rhythm. Neuroscience 156:238-46
Wang, Xue; Chen, Yonghong; Ding, Mingzhou (2008) Estimating Granger causality after stimulus onset: a cautionary note. Neuroimage 41:767-76
Rajagovindan, Rajasimhan; Ding, Mingzhou (2008) Decomposing neural synchrony: toward an explanation for near-zero phase-lag in cortical oscillatory networks. PLoS One 3:e3649
Bollimunta, Anil; Chen, Yonghong; Schroeder, Charles E et al. (2008) Neuronal mechanisms of cortical alpha oscillations in awake-behaving macaques. J Neurosci 28:9976-88

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